TensorFlow plugins

Migrates AI models and custom operators to Huawei Ascend NPUs, handles GPU-to-NPU code adaptation, profiling, performance optimization, distributed training deployment, and provides developer tooling for vLLM inference, Ascend C/Triton kernel development, and environment diagnostics.

Automate end-to-end ML performance investigations: research SOTA papers and architectures, generate phased plans, judge experimental methodologies, profile bottlenecks, run metric-improvement campaigns with atomic git commits, auto-rollback on regressions, and leverage specialist agents for data lifecycle and deep paper analysis.

Guardrail your AI/ML research workflow with an AI collaborator that searches literature using query variations, analyzes codebases and logs, designs minimal falsification experiments, records predictions, and audits bugs.

Delegate expert-level AI/ML workflows to specialized agents: engineer optimized prompts with evaluation and A/B testing, architect scalable LLM systems with RAG/LoRA fine-tuning, build production NLP pipelines for NER/classification/QA, and deploy optimized models via vLLM/Triton/Docker/K8s for reliability, performance, and cost control.

Mandatory adversarial review for Claude Code via 16 expert sub-agents (senior engineers, PM, security, data architects, SRE, ML engineers, etc.) that automatically push back on architectural decisions, test gaps, security holes, and requirements ambiguity before code is committed.

Apply 97 structured reasoning patterns from history's greatest thinkers to any problem — debug, design, research, or write — using specialized agents that analyze, critique, and synthesize across domains.